Intelligent Travel Recommendation System by Mining Attributes from Community Contributed Photos

Subramaniyaswamy, V.; Vijayakumar, V.; Ravi, Logesh; Indragandhi, V.

doi:10.1016/j.procs.2015.04.014

Cited by 36 publications

(12 citation statements)

References 15 publications

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“…In addition, memorybased CF is divided into two categories; user-based CF, which considers user similarity for recommendation [35][36][37] and item-based CF, which considers the item similarity for recommendation [38,39]. Data mining techniques such as neural networks [40], Naïve Bayesian modeling [13,23], association rule mining [41], and SVD [2] are used for model-based CF.…”

Section: Related Workmentioning

confidence: 99%

Developing a Contextually Personalized Hybrid Recommender System

Bozanta

Kutlu

2018

Mobile Information Systems

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It is hard to choose places to go from an endless number of options for some specific circumstances. Recommender systems are supposed to help us deal with these issues and make decisions that are more appropriate. The aim of this study is to recommend new venues to users according to their preferences. For this purpose, a hybrid recommendation model is proposed to integrate user-based and item-based collaborative filtering, content-based filtering together with contextual information in order to get rid of the disadvantages of each approach. Besides that, in which specific circumstances the user will like a specific venue is predicted for each user-venue pair. Moreover, threshold values determining the user’s liking toward a venue are determined separately for each user. Results are evaluated with both offline experiments (precision, recall, F-1 score) and a user study. Both the experimental evaluation with a real-world dataset and a user study of the proposed system showed improvement upon the baseline approaches.

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Section: Related Workmentioning

confidence: 99%

Developing a Contextually Personalized Hybrid Recommender System

Bozanta

Kutlu

2018

Mobile Information Systems

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“…This paper also describes an algorithm adaboost to classify data and Bayesian learning model for predicting the user's desired position based on his/her preferences. This is a probabilistic travel recommendation model that uses user-supplied photo tags to automatically mine knowledge, character attributes detected in photo content, travel group types, and travel group seasons [8].…”

Section: Related Workmentioning

confidence: 99%

Survey: Optimal Travel Route Discovery based on Topic Interest and Image Attributes

Murali¹,

A²

2018

IJTSRD

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With the popularity of social media such as Facebook and Flicker, users can easily share their registration records and photos during their travels. When planning a trip, users always have a particular preference for their trip. The system does not restrict users to limited query options (such as location, activity, or time period), but rather treats any text description as a keyword for personalized needs. Previous work has elaborated on excavating and arranging existing routes from check-in data. In order to meet the needs of the auto travel organization, the system claims that more POIs should be extracted. Therefore, this paper proposes an efficient keyword based representative travel route framework that uses knowledge from historical flow records and socia interactions of users. Explicitly, the system designs a keyword extraction module to classify POI tags so as to effectively match the query keywords. The system further designed a route reconstruction algorithm to build route candidates that meet requirements. In order to provide appropriate search results, explore the representative Skyline concept, the Skyline route, which best describes the tradeoffs between different POI features. In order to evaluate the effectiveness and efficiency of th algorithm, extensive experiments were conducted on location-based real social network datasets. However, it does not consider the cost of travel routes. So use the minimum spanning tree to calculate cost travel routes.

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“…A place is mined from available user contributed photos of that place available on photo sharing websites. [15]. Q. Liu presents personalized travel recommendation exploiting online travel information.…”

Section: H Huang Describes Collaborative Filtering To Mine Gps Trajementioning

confidence: 99%

“…Luepol Pipanmekaporn proposed a novel user-based collaborative POI recommendation algorithm for location-based social networks [16]. This method focuses on inferring user's check-in behaviors from user's location history to make a high-quality recommendation of POIs to a user based on the current location and opinions of similar users [15]. …”

Section: H Huang Describes Collaborative Filtering To Mine Gps Trajementioning

confidence: 99%

Survey on Recommendation of Personalized Travel Sequence

Aswale¹,

Dharmadhikari²

2017

International Journal of Advanced Research in Computer and Comm

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Now a day, traveling recommendation is important for user who is the plan for traveling. There are many existing techniques which are used for travel recommendation. In this paper explain a personalized travel sequence recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. It recommends personalized users travel interest and recommend a sequence of travel interest instead of an individual point of interest. The existing system cannot complete the requirement i.e. personalized and sequential recommendation together. To solve the problem of providing personalized and sequential travel package recommendation, a topical package model is created using social media data in which automatically mine user travel interest with another attribute like time, cost, and season of traveling. The proposed system uses the travelogues and photos of social media which map each user and routes description to the topical package area to induce user topical package model and route topical package model. To suggest personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Then high stratified routes are more optimized by using social similar users travel records for more accuracy.

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Intelligent Travel Recommendation System by Mining Attributes from Community Contributed Photos

Cited by 36 publications

References 15 publications

Developing a Contextually Personalized Hybrid Recommender System

Developing a Contextually Personalized Hybrid Recommender System

Survey: Optimal Travel Route Discovery based on Topic Interest and Image Attributes

Survey on Recommendation of Personalized Travel Sequence

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